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In elderly patients with cardiac diseases, changes in cardiovascular physiology diminish cardiovascular reserve and predispose to hemodynamic instability after spinal anesthesia; hence, such patients could be at risk of postoperative complications. Additionally, transthoracic echocardiography (TTE) is used in clinical practice to evaluate cardiovascular hemodynamics. Therefore, we hypothesized that echocardiographic measurements could display significant diagnostic power in the prediction of post - spinal anesthesia hypotension in elderly patients with cardiac diseases and reduced left ventricular ejection fraction (LV-EF). Therefore, sixty-one elderly orthopedic-trauma patients were recruited. Prior to spinal anesthesia a TTE examination was performed. The LV-EF, the stroke volume index (SVI), the peripheral vascular resistance (PVR), the LV filling pressures (E/Em ratio), the right ventricular function [tricuspid annular plane systolic excursion (TAPSE), tricuspid annular systolic velocity (TASV) and fractional area change (FAC)], as well as inferior vena cava (IVC) measurements, such as IVCCI (collapsibility index of the IVC) and dIVCmax (maximum diameter of IVC)-to-IVCCI ratio were assessed. Twenty-six out of sixty-one patients manifested hypotension. Preoperative dIVCmax-to-IVCCI ratio demonstrated the greatest performance amongst echocardiographic indices in predicting post - spinal anesthesia hypotension. The dIVCmax-to-IVCCI ratio 0.28, FAC > 42, E/Em ratio

Although several studies have utilized AI (artificial intelligence)-based solutions to enhance the decision making for mechanical ventilation, as well as, for mortality in COVID-19, the extraction of explainable predictors regarding heparin's effect in intensive care and mortality has been left unresolved. In the present study, we developed an explainable AI (XAI) workflow to shed light into predictors for admission in the intensive care unit (ICU), as well as, for mortality across those hospitalized COVID-19 patients who received heparin. AI empowered classifiers, such as, the hybrid Extreme gradient boosting (HXGBoost) with customized loss functions were trained on time-series curated clinical data to develop robust AI models. Shapley additive explanation analysis (SHAP) was conducted to determine the positive or negative impact of the predictors in the model's output. The HXGBoost predicted the risk for intensive care and mortality with 0.84 and 0.85 accuracy, respectively. SHAP analysis indicated that the low percentage of lymphocytes at day 7 along with increased FiO

Objective:Olfactory and gustatory dysfunction that relates with the infection from severe acute respiratory syndrome-related coronavirus (SARS-CoV-2) has already improved. The relation between chemosensory dysfunction and age and gender in covid-19 positive patients is the main objective of the present study. Methods:We used a questionnaire to select information about medical history, patient demographics and reported symptoms during infection. Three hundred covid-19 positive patients, who underwent a RT-PCR test in the University Hospital of Ioannina, Grecce, were included in this study; 150 of them recovered at home and the remaining 150 were admitted to hospital. Statistical analysis based on ÉBM-SPSS Statistics 26.0 was done. Results:The total sample included 300 patients, of which 106 females and 194 males. There was a statistically significant difference between the subgroup of patients aged 21-25, 61-65 and 71-75 with loss of smell, that of hospitalized patients aged 41-45 with loss of smell and the subgroup of those aged 31-35 and 71-75 with loss of taste. Conclusion:There is a significant association between chemosensory dysfunction and younger age groups. Olfactory and gustatory dysfunction appears more frequently in women than men. Male gender relates with disease severity.